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Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings

Junkun Chen, Renjie Zheng, Atsuhito Kita, Mingbo Ma, Liang Huang

2021Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing14 citationsDOIOpen Access PDF

Abstract

Simultaneous translation is vastly different from full-sentence translation, in the sense that it starts translation before the source sentence ends, with only a few words delay. However, due to the lack of large-scale, high-quality simultaneous translation datasets, most such systems are still trained on conventional fullsentence bitexts. This is far from ideal for the simultaneous scenario due to the abundance of unnecessary long-distance reorderings in those bitexts. We propose a novel method that rewrites the target side of existing fullsentence corpora into simultaneous-style translation. Experiments on Zh!En and Ja!En simultaneous translation show substantial improvements (up to +2.7 BLEU) with the addition of these generated pseudo-references.

Topics & Concepts

Translation (biology)Computer scienceSentenceMachine translationNatural language processingArtificial intelligenceIdeal (ethics)Style (visual arts)Speech recognitionHistoryPhilosophyEpistemologyMessenger RNAGeneBiochemistryChemistryArchaeologyNatural Language Processing TechniquesTopic ModelingText Readability and Simplification
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